Even a billion dogs could not do this. Nor could 3^^^3 nematodes. This belief is just plain unintelligent.
That was a straw man, though. The idea was to scale up a small brain into a big brain - not to put lots of small brains together.
You simply don't need to understand how an adult brain works in order to build something with superior functionality.
... I am incomprehensible of how one could come to this belief.
Right - so: this has already been done in many domains - e.g. chess. Engineers will just mop up the remaining domains without bothering with the daft and unnecessary task of reverse engineering the human brain.
No, but we did need to know how flight works to build a flying machine. [...] What we don't know is how cognition / intelligence works.
Well, we do know what the equivalent of "lift" is. It's inductive inference. See: Inductive inference is like lift. We can already generate the equivalent of lift. We just don't yet know how to get a lot of it in one place.
Do you understand the conceptual difference between narrow intelligence and general intelligence? AI researchers gave up decades ago on the notion of narrow AI yielding general AI.
No, they didn't. A few researchers did that, in an attempt to distinguish themselves from the mainstream.
So yes. I can't see any progress at all to speak of. And yes, I know of Watson, Siri, Google, etc., etc..
So: I think progress is happening. It looks something like: this and this. Machines already make most stockmarket trades, can translate languages and do speech recognition, and bots have conquered manufacturing and are busy invading retail outlets, banks, offices and call centres. One wonders what it would take for you to classify something as progress towards machine intelligence.
I can "scale up" a threaded program by giving more processors for the threads to run on, but this doesn't actually improve the program output (apart from rounding error and nondeterministic effects), it just makes the output faster. I can "scale up" an approximation algorithm that has a variable discretization size N, and that actually improves the output... but how do you adjust "N" in a worm brain?
If you were a utilitarian, then why would you want to risk creating an AGI that had the potential to be an existential risk, when you could eliminate all suffering with the advent of WBE (whole brain emulation) and hence virtual reality (or digital alteration of your source code) and hence utopia? Wouldn't you want to try to prevent AI research and just promote WBE research? Or is it that AGI is more likely to come before WBE and so we should focus our efforts on making sure that the AGI is friendly? Or maybe uploading isn't possible for technological or philosophical reasons (substrate dependence)?
Is there a link to a discussion on this that I'm missing out on?